Background and purposeCorpus callosum (CC) infarction is an extremely rare subtype of cerebral ischemic stroke, however, the symptoms of cognitive impairment often fail to attract early attention of patients, which seriously affects the long-term prognosis, such as high mortality, personality changes, mood disorders, psychotic reactions, financial burden and so on. This study seeks to develop and validate models for early predicting the risk of subjective cognitive decline (SCD) after CC infarction by machine learning (ML) algorithms.MethodsThis is a prospective study that enrolled 213 (only 3.7%) CC infarction patients from a nine-year cohort comprising 8,555 patients with acute ischemic stroke. Telephone follow-up surveys were carried out for the patients with definite diagnosis of CC infarction one-year after disease onset, and SCD was identified by Behavioral Risk Factor Surveillance System (BRFSS) questionnaire. Based on the significant features selected by the least absolute shrinkage and selection operator (LASSO), seven ML models including Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), Gaussian Naïve Bayes (GNB), Complement Naïve Bayes (CNB), and Support vector machine (SVM) were established and their predictive performances were compared by different metrics. Importantly, the SHapley Additive exPlanations (SHAP) was also utilized to examine internal behavior of the highest-performance ML classifier.ResultsThe Logistic Regression (LR)-model performed better than other six ML-models in SCD predictability after the CC infarction, with the area under the receiver characteristic operator curve (AUC) of 77.1% in the validation set. Using LASSO and SHAP analysis, we found that infarction subregions of CC infarction, female, 3-month modified Rankin Scale (mRS) score, age, homocysteine, location of angiostenosis, neutrophil to lymphocyte ratio, pure CC infarction, and number of angiostenosis were the top-nine significant predictors in the order of importance for the output of LR-model. Meanwhile, we identified that infarction subregion of CC, female, 3-month mRS score and pure CC infarction were the factors which independently associated with the cognitive outcome.ConclusionOur study firstly demonstrated that the LR-model with 9 common variables has the best-performance to predict the risk of post-stroke SCD due to CC infarcton. Particularly, the combination of LR-model and SHAP-explainer could aid in achieving personalized risk prediction and be served as a decision-making tool for early intervention since its poor long-term outcome.

On the bases of an analysis of the reasons for the low the working principle of the driving mcchanisrn and analyses kinematic accuracy and poor stiffness of the conventional robots, of loading capacity and kinematic accuracy. The numerical a new type of machining robot that is particularly adapted to control system and relevant algorithm, simulation analysis of perform cutting tasks is developed in this study. A new type of 4-the overall characteristics of the robot, intelligent axis combined driving arm is presented and its principle is compensation control of machining accuracy and cutting firstly introduced in this paper, and the mechanical experiment will be presented in other research papers performance is analyzed. Analytical results show that the successively. mechanism is characterized by high stiffness and good kinematic accuracy. 11. 'INFLUENCE FACTORS ON THE STIFFNESS AND KINEMATIC ACCURACY OF ROBOTS 1. INTRODUCTIONWith good flexibility and large workspace, industrial robots have been employed widely in various fields. Recently, it is an active area to apply robot to perform machining tasks, for instance, to use vertical multi-articulated robots to grind, abrade, deburr, scrape, and so forth[ 1],[2],[3]. Also, robots are employed to machine complex shaped faces. All of these illustrate the good flexibility of robots. However, these researches have not got any breakthrough in the sense of low kinematic accuracy and poor stiffness of robots, especially the poor cross-load bearing capacity in the direction perpendicular to the vertical plane, which is disadvantageous for cutting processes since it will lower the machining accuracy and it is easy to cause cutting vibration and decrease machining efficiency.The reasons for poor kinematic precision and low stiffness of conventional robots are firstly analyzed in this study. A new combined 4-axis driven arm is presented based on this analysis. A new type of machining robot is then constructed with this combined mechanism. This paper mainly deals with In order to make robots dexterous (i.e., with high flexibility) and with large work envelope, the structure of generalpurpose robots is characterized by: i) multi-joint(usual1y rotary joints), and ii) long and thin arm. The influence factors on the stiffness and kinematic accuracy are analyzed in the following by taking a robot arm as an example. A. Influence Factors on Arm StiflnessThe stifhess of a robot arm is determined by the stiffness of the links and joints.